A loose federation of distributed, typed datasets
chore(release): complete v0.2.2 beta review with docs, tests, and code improvements
- Add Lower-Level Loaders documentation (SchemaLoader, DatasetLoader, LensLoader)
- Document __orig_class__ assumption in Dataset and SampleBatch docstrings
- Add comprehensive to_parquet() documentation with memory usage warning
- Add troubleshooting and deployment guide pages to docs navigation
- Centralize tar creation helpers in conftest.py (create_tar_with_samples, etc.)
- Add shared sample types to reduce test duplication
- Expand error handling tests with timeout and partial failure scenarios
- Add S3 error simulation tests for access denied and connection timeout
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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# atdata v0.2.2 Beta Release - Comprehensive Review
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**Date:** 2026-01-22
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**Branch:** feature/human-review
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**Reviewer:** Claude Opus 4.5
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---
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## Executive Summary
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**Overall Assessment: Production-Ready Beta**
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The atdata codebase demonstrates excellent engineering practices with strong architecture, comprehensive documentation, and thorough testing. The library is well-suited for a v0.2.2 beta release.
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| Category | Rating | Notes |
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|----------|--------|-------|
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| **Architecture** | ⭐⭐⭐⭐⭐ | Clean layered design, protocol-driven extensibility |
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| **Code Quality** | ⭐⭐⭐⭐⭐ | No code smells, excellent type safety |
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| **Documentation** | ⭐⭐⭐⭐⭐ | Comprehensive docstrings, clear examples |
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| **Test Suite** | ⭐⭐⭐⭐ | 1,172 tests, good coverage, some consolidation needed |
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| **Docs Website** | ⭐⭐⭐⭐ | Well-organized, one incomplete page |
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| **Examples** | ⭐⭐⭐⭐⭐ | Three runnable examples with mock fallbacks |
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---
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## Part 1: Codebase Architecture & Code Quality
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### 1.1 Architecture Overview
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**Total Size:** ~5,661 lines across 19 Python modules, 64 classes, 291 functions
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The codebase follows a clean layered architecture:
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```
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src/atdata/
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├── Core Layer (dataset management)
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│ ├── dataset.py - Main Dataset, PackableSample, SampleBatch (767 lines)
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│ ├── lens.py - Type transformations and LensNetwork registry (295 lines)
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│ ├── _helpers.py - NumPy array serialization utilities
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│
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├── Protocol/Interface Layer
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│ ├── _protocols.py - Abstract protocols (Packable, IndexEntry, etc.) (434 lines)
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│ ├── _sources.py - DataSource implementations (URLSource, S3Source) (322 lines)
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│
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├── Infrastructure Layer
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│ ├── _type_utils.py - Shared type conversion utilities (91 lines)
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│ ├── _schema_codec.py - Dynamic type generation from schemas (435 lines)
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│ ├── _cid.py - ATProto-compatible CID generation (141 lines)
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│ ├── _hf_api.py - HuggingFace-style dataset loading API (654 lines)
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│ ├── _stub_manager.py - Type stub generation for IDEs
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│ ├── promote.py - Local-to-atmosphere promotion workflow (197 lines)
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│
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├── local.py - Redis + S3 local index backend (~1800 lines)
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│
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└── atmosphere/ - ATProto federation integration (278+ lines)
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├── __init__.py - Unified index interface
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├── client.py - ATProto authentication
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├── schema.py - Schema publishing/loading
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├── records.py - Dataset publishing/loading
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├── lens.py - Lens publishing/loading
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└── _types.py - ATProto record type definitions
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```
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### 1.2 Key Design Patterns
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#### Generic Type Parameters with Runtime Extraction
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- **Location:** `dataset.py:287, 396`
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- `SampleBatch[DT]` and `Dataset[ST]` extract type parameters at runtime via `typing.get_args(__orig_class__)[0]`
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- Properly cached to avoid repeated calls
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- **Minor risk:** Assumes instances created via `Dataset[T](...)` syntax
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#### Singleton Pattern for LensNetwork
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- **Location:** `lens.py:228-255`
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- Thread-safe implementation with lazy initialization
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- Good pattern for global registry, used correctly throughout
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#### @packable Decorator
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- **Location:** `dataset.py:706-767`
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- Transforms classes to dataclass + PackableSample subclass
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- Preserves original class identity for IDE support
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- Creative solution combining decorator simplicity with type safety
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#### DataSource Protocol Abstraction
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- **Location:** `_sources.py`, `_protocols.py:348-421`
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- Enables pluggable backends (URLSource, S3Source, future extensions)
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- Clean separation between data fetching and serialization
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### 1.3 Code Quality Assessment
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#### Strengths
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**Documentation & Docstrings:** ⭐⭐⭐⭐⭐
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- 18/19 modules have module-level docstrings
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- All public classes have comprehensive docstrings with Args, Returns, Raises, Examples
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- Excellent module docstring in `dataset.py` (lines 1-26) with usage examples
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**Type Safety:** ⭐⭐⭐⭐⭐
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- ~95% type hint coverage on public APIs
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- Proper use of `TypeVar`, `Generic[ST]`, `@runtime_checkable` protocols
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- Well-defined type aliases: `Pathlike`, `WDSRawSample`, `SampleExportMap`
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**Error Handling:** ⭐⭐⭐⭐⭐
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- Meaningful exceptions: `ValueError`, `TypeError`, `KeyError`, `RuntimeError`
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- No bare `except:` clauses
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- HTTP errors checked with `raise_for_status()`
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**No Code Smells:**
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- ❌ No star imports
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- ❌ No `NotImplementedError` (only legitimate Protocol stubs)
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- ❌ No `eval`/`exec` in user code
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- ❌ No bare `except:` clauses
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- ❌ No print() debugging
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- ❌ No TODO/FIXME/HACK comments
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### 1.4 Module-Specific Findings
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#### dataset.py (767 lines) - CORE
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- **Quality:** ⭐⭐⭐⭐⭐
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- `PackableSample`: Well-designed base class with automatic NDArray conversion
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- `SampleBatch[DT]`: Smart aggregation with caching
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- `Dataset[ST]`: Backward-compatible, lazy metadata loading
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- **Note:** `to_parquet()` loads full dataset into memory - document this
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#### lens.py (295 lines) - TRANSFORMATION SYSTEM
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- **Quality:** ⭐⭐⭐⭐
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- Parameter validation via `inspect.signature`
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- Clean `putter()` decorator pattern
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- **Limitation:** No lens composition/chaining yet
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#### _sources.py (322 lines) - DATA BACKENDS
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- **Quality:** ⭐⭐⭐⭐⭐
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- Lazy boto3 client initialization
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- Multiple constructor patterns
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- No hardcoded endpoints
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#### local.py (~1800 lines) - LOCAL STORAGE
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- **Quality:** ⭐⭐⭐⭐
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- **Concern:** Large file - candidate for splitting into index.py, schema.py, storage.py
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- Clean abstractions for LocalDatasetEntry, SchemaNamespace
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### 1.5 Public API Surface
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**Exports in `__init__.py`:**
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- Core: `PackableSample`, `SampleBatch`, `Dataset`, `Lens`, `LensNetwork`, `@packable`
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- Protocols: `IndexEntry`, `AbstractIndex`, `AbstractDataStore`, `DataSource`
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- Sources: `URLSource`, `S3Source`
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- Utilities: `load_dataset`, `DatasetDict`, `schema_to_type`, `generate_cid`, `verify_cid`, `promote_to_atmosphere`
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**Assessment:** API is clean, well-documented, and appropriately scoped.
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---
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## Part 2: Test Suite Quality
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### 2.1 Overview Statistics
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- **Total Tests:** 1,172 across 22 test files
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- **Total Test Code:** ~13,227 lines
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- **Approach:** Heavy parametrization, fixtures, integration tests, edge case coverage
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### 2.2 Test File Summary
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| File | Tests | Focus |
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|------|-------|-------|
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| test_atmosphere.py | 205 | ATProto/Atmosphere protocol, schema/dataset publishers |
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| test_hf_api.py | 174 | HuggingFace integration, dataset resolution, glob patterns |
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| test_local.py | 133 | Local storage, Redis-backed indexes, S3 datastores |
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| test_integration_dynamic_types.py | 68 | Schema-to-type reconstruction, caching |
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| test_integration_local.py | 56 | End-to-end Repo workflows |
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| test_integration_edge_cases.py | 54 | Boundary conditions, unicode, empty datasets |
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| test_sources.py | 52 | URLSource, S3Source implementations |
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| test_integration_cross_backend.py | 50 | Local ↔ Atmosphere interoperability |
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| test_integration_e2e.py | 46 | Full end-to-end workflows |
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| test_integration_error_handling.py | 44 | Error conditions, malformed data |
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| test_dataset.py | 19 | Core Dataset, SampleBatch, serialization |
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| test_lens.py | 5 | Lens laws (GetPut/PutGet/PutPut) |
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| Others | ~60+ | Protocols, CID, helpers, promotion |
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### 2.3 Coverage Analysis
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#### Well-Covered Modules
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- **dataset.py:** Sample creation, serialization, batching, iteration, parquet export
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- **atmosphere/:** Schema publishing, dataset publishing, lens publishing, ATUri parsing
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- **local.py:** LocalDatasetEntry, index queries, schema publishing, S3DataStore
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#### Under-Tested Modules
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- `_type_utils.py`: No dedicated tests (used indirectly)
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- `_schema_codec.py`: No dedicated unit tests (covered in integration)
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- `_stub_manager.py`: No tests found
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### 2.4 Test Quality Assessment
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#### Strengths
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- **Comprehensive Parametrization:** Heavy use of `@pytest.mark.parametrize`
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- **Good Fixture Design:** Automatic cleanup, shared samples in conftest.py
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- **Edge Case Coverage:** Empty arrays, scalar arrays, unicode, all primitive types, malformed data
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- **Appropriate Mocking:** Moto for S3, unittest.mock for ATProto
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#### Weaknesses
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- **Duplicate Sample Types:** Multiple files define similar types (BasicSample, SimpleTestSample)
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- **Repeated Helpers:** `create_tar_with_samples()` defined in 3+ files
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- **60 filterwarnings:** Suppressing s3fs/moto async warnings (should fix at source)
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- **Missing Coverage:** Network timeouts, concurrent access, partial failures
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### 2.5 Efficiency Opportunities
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1. **Consolidate Sample Types** - Move to conftest.py
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2. **Centralize Tar Creation** - Create shared `create_test_tar()` fixture
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3. **Deduplicate Mock Setup** - Share mock_atproto_client across files
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4. **Add Performance Markers** - `@pytest.mark.slow`, `@pytest.mark.network`
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### 2.6 Missing Test Scenarios
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- Network timeouts and retries
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- Partial S3 failures (multi-shard, one fails)
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- Redis connection drops mid-operation
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- Schema evolution (backward/forward compatibility)
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- Concurrent dataset operations
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- Memory pressure with very large batches
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---
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## Part 3: Documentation Website & Examples
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### 3.1 Documentation Structure
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**Generator:** Quarto static site
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**Source:** `docs_src/` (markdown .qmd files)
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**Output:** `docs/` (generated HTML)
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**Organization:**
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- Main index page + 4 tutorials + 9 reference pages + 1 API reference
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- Clear hierarchy: Guide → Tutorials → Reference → API
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- Good navigation with sidebar and navbar
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### 3.2 Content Quality
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**Strengths:**
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- Documentation **matches current code** (verified)
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- Examples use correct API patterns
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- Clear explanation of concepts (typed samples, lenses, lens laws)
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- Strong emphasis on type safety and Python 3.12+ features
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- Good use of callouts, admonitions, and tabbed examples
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**Verified Consistency:**
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- ✅ `@packable` decorator usage matches implementation
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- ✅ `@lens` decorator behavior documented accurately
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- ✅ `Dataset[Type]` generic syntax matches source
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- ✅ LocalIndex API methods match implementation
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- ✅ Exports in `__init__.py` match documented modules
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### 3.3 Example Files
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Three well-documented Python scripts in `/examples/`:
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| File | Lines | Coverage | Runnable |
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|------|-------|----------|----------|
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| atmosphere_demo.py | 464 | Type introspection, AT URI, schema building, blob storage | ✅ |
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| local_workflow.py | 313 | LocalIndex, LocalDatasetEntry, S3DataStore, Redis | ✅ |
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| promote_workflow.py | 407 | Promotion, schema deduplication, data migration | ✅ |
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All examples have `--help` support and graceful fallbacks for missing services.
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### 3.4 Documentation Gaps
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**High Priority:**
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1. **Incomplete atmosphere.qmd** - Reference page truncated at "Lower-Level Publishers"
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2. **No Troubleshooting Guide** - FAQ section missing
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3. **No Deployment Guide** - Production setup not documented
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4. **Schema Versioning Strategy** - Not documented
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**Medium Priority:**
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1. Performance tuning guide with benchmarks
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2. Lens composition examples
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3. Testing examples for PackableSample types
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4. Custom DataSource implementation tutorial
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**Low Priority:**
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1. Migration guide for schema URI format changes
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2. More complex real-world examples
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3. Internal implementation documentation
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### 3.5 Documentation Coverage by Component
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| Component | Status | Notes |
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|-----------|--------|-------|
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| PackableSample | ✅ Excellent | Complete |
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| Dataset | ✅ Excellent | Could use performance tuning |
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| Lenses | ✅ Good | Composition examples missing |
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| load_dataset | ✅ Good | More split examples needed |
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| LocalIndex | ✅ Good | Schema versioning missing |
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| AtmosphereClient | ⚠️ Incomplete | Reference page truncated |
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| Protocols | ✅ Adequate | Custom impl examples sparse |
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---
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## Part 4: Actionable Recommendations
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### 4.1 High Priority (Before Beta Release)
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| # | Category | Issue | Action |
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|---|----------|-------|--------|
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| 1 | Docs | Incomplete atmosphere.qmd | Complete the truncated reference page |
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| 2 | Tests | 60 filterwarnings suppressions | Fix root cause of s3fs/moto async warnings |
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| 3 | Tests | Duplicate sample types | Consolidate to conftest.py |
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| 4 | Tests | Repeated tar creation helper | Create shared fixture |
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| 5 | Code | `__orig_class__` assumption | Document in Dataset docstring |
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### 4.2 Medium Priority (Post-Beta)
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| # | Category | Issue | Action |
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|---|----------|-------|--------|
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| 6 | Docs | No troubleshooting guide | Add FAQ/common errors section |
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| 7 | Docs | No deployment guide | Document production setup |
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| 8 | Tests | Missing error path tests | Add timeout, partial failure tests |
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| 9 | Tests | No performance markers | Add @pytest.mark.slow markers |
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| 10 | Code | local.py size (~1800 lines) | Consider splitting into modules |
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| 11 | Code | Document lens registration timing | Clarify thread-safety expectations |
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| 12 | Code | Document to_parquet() memory | Add note about full dataset loading |
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### 4.3 Low Priority (Future)
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| # | Category | Issue | Action |
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|---|----------|-------|--------|
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| 13 | Docs | Schema versioning strategy | Document best practices |
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| 14 | Docs | Performance tuning guide | Add benchmarks and recommendations |
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| 15 | Tests | Concurrent access tests | Add multi-process scenarios |
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| 16 | Tests | Schema evolution tests | Add backward compatibility tests |
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| 17 | Code | Schema reference support | Add 'ref' type to _schema_codec.py |
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---
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## Part 5: Summary Statistics
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### Codebase
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- **19 Python modules**, ~5,661 lines
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- **64 classes**, **291 functions**
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- **~95% type hint coverage**
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- **0 code smells** detected
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### Test Suite
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- **22 test files**, **1,172 tests**
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- **~13,227 lines** of test code
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- **Good coverage** of core functionality
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- **Edge cases well-tested**
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### Documentation
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- **14 pages** (1 index + 4 tutorials + 9 references)
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- **3 runnable examples** (1,184 total lines)
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- **Strong code-documentation alignment**
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- **1 incomplete page** (atmosphere.qmd)
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---
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## Conclusion
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The atdata library is **ready for v0.2.2 beta release** with the following caveats:
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1. Complete the truncated `atmosphere.qmd` documentation
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2. Address the test suite efficiency issues (consolidate fixtures, fix filterwarnings)
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3. Add missing documentation notes about memory usage and thread safety
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The codebase demonstrates professional engineering quality with excellent architecture, comprehensive testing, and clear documentation. The identified issues are minor and appropriate for a beta release.
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CHANGELOG.md
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CHANGELOG.md
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- **Comprehensive integration test suite**: 593 tests covering E2E flows, error handling, edge cases
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### Changed
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- v0.2.2 beta release improvements (#326)
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- Document to_parquet() memory usage (#336)
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- Evaluate splitting local.py into modules (#335)
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- Add error path tests (timeouts, partial failures) (#334)
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- Add deployment guide to docs (#333)
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- Add troubleshooting/FAQ section to docs (#332)
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- Document __orig_class__ assumption in Dataset docstring (#331)
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- Centralize tar creation helper in test fixtures (#330)
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- Consolidate duplicate test sample types to conftest.py (#329)
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- Document expected filterwarnings in test suite (#328)
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- Complete truncated atmosphere.qmd documentation (#327)
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- Comprehensive v0.2.2 beta release review (#321)
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- Compile findings into .review/comprehensive-review.md (#325)
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- Review documentation website and examples (#324)
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- Review test suite coverage and quality (#323)
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- Review core codebase architecture and code quality (#322)
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- Human Review: Local Workflow API Improvements (#274)
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- Update documentation and examples for current codebase (#316)
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- Update README.md with current API (#320)
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- Update examples/*.py files for current API (#319)
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docs_src/_quarto.yml
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docs_src/_quarto.yml
···
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href: reference/protocols.qmd
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- text: "URI Specification"
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href: reference/uri-spec.qmd
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- text: "Troubleshooting & FAQ"
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href: reference/troubleshooting.qmd
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- text: "Deployment Guide"
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href: reference/deployment.qmd
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- text: "API"
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href: api/index.qmd
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right:
···
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- reference/load-dataset.qmd
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- reference/protocols.qmd
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- reference/uri-spec.qmd
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- reference/troubleshooting.qmd
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- reference/deployment.qmd
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- section: "API Reference"
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contents:
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- api/index.qmd
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docs_src/reference/atmosphere.qmd
+74
docs_src/reference/atmosphere.qmd
···
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)
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```
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## Lower-Level Loaders
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For direct access to records, use the loader classes:
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### SchemaLoader
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```{python}
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#| eval: false
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from atdata.atmosphere import SchemaLoader
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loader = SchemaLoader(client)
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# Get a specific schema
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schema = loader.get("at://did:plc:abc/ac.foundation.dataset.sampleSchema/xyz")
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print(schema["name"], schema["version"])
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# List all schemas from a repository
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for schema in loader.list_all(repo="did:plc:other-user"):
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print(schema["name"])
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```
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### DatasetLoader
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```{python}
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#| eval: false
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from atdata.atmosphere import DatasetLoader
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loader = DatasetLoader(client)
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# Get a specific dataset record
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record = loader.get("at://did:plc:abc/ac.foundation.dataset.record/xyz")
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# Check storage type
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storage_type = loader.get_storage_type(uri) # "external" or "blobs"
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# Get URLs based on storage type
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if storage_type == "external":
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urls = loader.get_urls(uri)
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else:
311
+
urls = loader.get_blob_urls(uri)
312
+
313
+
# Get metadata
314
+
metadata = loader.get_metadata(uri)
315
+
316
+
# Create a Dataset object directly
317
+
dataset = loader.to_dataset(uri, MySampleType)
318
+
for batch in dataset.ordered(batch_size=32):
319
+
process(batch)
320
+
```
321
+
322
+
### LensLoader
323
+
324
+
```{python}
325
+
#| eval: false
326
+
from atdata.atmosphere import LensLoader
327
+
328
+
loader = LensLoader(client)
329
+
330
+
# Get a specific lens record
331
+
lens = loader.get("at://did:plc:abc/ac.foundation.dataset.lens/xyz")
332
+
print(lens["name"])
333
+
print(lens["sourceSchema"], "->", lens["targetSchema"])
334
+
335
+
# List all lenses from a repository
336
+
for lens in loader.list_all():
337
+
print(lens["name"])
338
+
339
+
# Find lenses by schema
340
+
lenses = loader.find_by_schemas(
341
+
source_schema_uri="at://did:plc:abc/ac.foundation.dataset.sampleSchema/source",
342
+
target_schema_uri="at://did:plc:abc/ac.foundation.dataset.sampleSchema/target",
343
+
)
344
+
```
345
+
272
346
## AT URIs
273
347
274
348
ATProto records are identified by AT URIs:
+329
docs_src/reference/deployment.qmd
+329
docs_src/reference/deployment.qmd
···
1
+
---
2
+
title: "Deployment Guide"
3
+
description: "Production deployment for local storage and ATProto integration"
4
+
---
5
+
6
+
This guide covers deploying atdata in production environments, including Redis setup for LocalIndex, S3 storage configuration, and ATProto publishing considerations.
7
+
8
+
## Local Storage Deployment
9
+
10
+
The local storage backend uses Redis for metadata indexing and S3-compatible storage for dataset files.
11
+
12
+
### Redis Setup
13
+
14
+
#### Requirements
15
+
16
+
- Redis 6.0+ (for Redis-OM compatibility)
17
+
- Sufficient memory for index metadata (typically < 100MB for most deployments)
18
+
19
+
#### Docker Deployment
20
+
21
+
```bash
22
+
# Basic Redis
23
+
docker run -d \
24
+
--name atdata-redis \
25
+
-p 6379:6379 \
26
+
-v redis-data:/data \
27
+
redis:7-alpine \
28
+
redis-server --appendonly yes
29
+
30
+
# With password
31
+
docker run -d \
32
+
--name atdata-redis \
33
+
-p 6379:6379 \
34
+
-v redis-data:/data \
35
+
redis:7-alpine \
36
+
redis-server --appendonly yes --requirepass yourpassword
37
+
```
38
+
39
+
#### Configuration
40
+
41
+
```python
42
+
from redis import Redis
43
+
from atdata.local import LocalIndex
44
+
45
+
# Basic connection
46
+
redis = Redis(host="localhost", port=6379)
47
+
index = LocalIndex(redis=redis)
48
+
49
+
# With authentication
50
+
redis = Redis(
51
+
host="redis.example.com",
52
+
port=6379,
53
+
password="yourpassword",
54
+
ssl=True, # For production
55
+
)
56
+
index = LocalIndex(redis=redis)
57
+
```
58
+
59
+
#### Redis Clustering
60
+
61
+
For high-availability deployments:
62
+
63
+
```python
64
+
from redis.cluster import RedisCluster
65
+
66
+
# Redis Cluster connection
67
+
redis = RedisCluster(
68
+
host="redis-cluster.example.com",
69
+
port=6379,
70
+
password="yourpassword",
71
+
)
72
+
index = LocalIndex(redis=redis)
73
+
```
74
+
75
+
::: {.callout-note}
76
+
Redis-OM (used internally) supports Redis Cluster mode. Ensure all nodes have the same configuration.
77
+
:::
78
+
79
+
### S3 Storage Setup
80
+
81
+
#### AWS S3
82
+
83
+
```python
84
+
from atdata.local import S3DataStore
85
+
86
+
# Using environment credentials (recommended for AWS)
87
+
# Set AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY
88
+
store = S3DataStore(
89
+
bucket="my-atdata-bucket",
90
+
prefix="datasets/",
91
+
)
92
+
93
+
# Explicit credentials
94
+
store = S3DataStore(
95
+
bucket="my-atdata-bucket",
96
+
prefix="datasets/",
97
+
credentials={
98
+
"AWS_ACCESS_KEY_ID": "...",
99
+
"AWS_SECRET_ACCESS_KEY": "...",
100
+
"AWS_DEFAULT_REGION": "us-west-2",
101
+
},
102
+
)
103
+
```
104
+
105
+
#### S3-Compatible Storage (MinIO, Cloudflare R2, etc.)
106
+
107
+
```python
108
+
store = S3DataStore(
109
+
bucket="my-bucket",
110
+
prefix="datasets/",
111
+
endpoint_url="https://s3.example.com",
112
+
credentials={
113
+
"AWS_ACCESS_KEY_ID": "...",
114
+
"AWS_SECRET_ACCESS_KEY": "...",
115
+
},
116
+
)
117
+
```
118
+
119
+
#### MinIO Deployment
120
+
121
+
```bash
122
+
# Docker deployment
123
+
docker run -d \
124
+
--name minio \
125
+
-p 9000:9000 \
126
+
-p 9001:9001 \
127
+
-v minio-data:/data \
128
+
-e MINIO_ROOT_USER=minioadmin \
129
+
-e MINIO_ROOT_PASSWORD=minioadmin \
130
+
minio/minio server /data --console-address ":9001"
131
+
```
132
+
133
+
```python
134
+
store = S3DataStore(
135
+
bucket="atdata",
136
+
endpoint_url="http://localhost:9000",
137
+
credentials={
138
+
"AWS_ACCESS_KEY_ID": "minioadmin",
139
+
"AWS_SECRET_ACCESS_KEY": "minioadmin",
140
+
},
141
+
)
142
+
```
143
+
144
+
### Production Checklist
145
+
146
+
- [ ] Redis persistence enabled (`appendonly yes`)
147
+
- [ ] Redis password authentication configured
148
+
- [ ] Redis TLS enabled for remote connections
149
+
- [ ] S3 bucket access policies configured (least privilege)
150
+
- [ ] S3 bucket versioning enabled (for data recovery)
151
+
- [ ] Monitoring for Redis memory usage
152
+
- [ ] Backup strategy for Redis data
153
+
154
+
## ATProto Deployment
155
+
156
+
### Account Setup
157
+
158
+
1. Create a Bluesky account or use your existing account
159
+
2. Generate an app-specific password at [bsky.app/settings/app-passwords](https://bsky.app/settings/app-passwords)
160
+
3. Never use your main account password in code
161
+
162
+
::: {.callout-warning}
163
+
**Security**: Always use app passwords, never your main password. App passwords can be revoked without affecting your account.
164
+
:::
165
+
166
+
### Authentication Patterns
167
+
168
+
#### Environment Variables (Recommended)
169
+
170
+
```python
171
+
import os
172
+
from atdata.atmosphere import AtmosphereClient
173
+
174
+
client = AtmosphereClient()
175
+
client.login(
176
+
os.environ["ATPROTO_HANDLE"],
177
+
os.environ["ATPROTO_APP_PASSWORD"],
178
+
)
179
+
```
180
+
181
+
#### Session Persistence
182
+
183
+
For long-running services, persist and reuse sessions:
184
+
185
+
```python
186
+
import os
187
+
from pathlib import Path
188
+
189
+
SESSION_FILE = Path("~/.atdata/session").expanduser()
190
+
191
+
client = AtmosphereClient()
192
+
193
+
if SESSION_FILE.exists():
194
+
# Restore existing session
195
+
session_string = SESSION_FILE.read_text()
196
+
try:
197
+
client.login_with_session(session_string)
198
+
except Exception:
199
+
# Session expired, re-authenticate
200
+
client.login(handle, app_password)
201
+
SESSION_FILE.parent.mkdir(parents=True, exist_ok=True)
202
+
SESSION_FILE.write_text(client.export_session())
203
+
else:
204
+
# Initial login
205
+
client.login(handle, app_password)
206
+
SESSION_FILE.parent.mkdir(parents=True, exist_ok=True)
207
+
SESSION_FILE.write_text(client.export_session())
208
+
```
209
+
210
+
### Custom PDS Deployment
211
+
212
+
For self-hosted ATProto infrastructure:
213
+
214
+
```python
215
+
client = AtmosphereClient(base_url="https://pds.example.com")
216
+
client.login("handle.example.com", "app-password")
217
+
```
218
+
219
+
See [ATProto PDS documentation](https://github.com/bluesky-social/pds) for self-hosting setup.
220
+
221
+
### Rate Limiting Considerations
222
+
223
+
ATProto has rate limits. For bulk operations:
224
+
225
+
- Space out record creation (1-2 per second for bulk uploads)
226
+
- Use batch operations where available
227
+
- Implement exponential backoff for retries
228
+
- Consider blob storage limits (~50MB per blob)
229
+
230
+
```python
231
+
import time
232
+
233
+
for i, dataset in enumerate(datasets_to_publish):
234
+
index.insert_dataset(dataset, name=f"dataset-{i}", ...)
235
+
time.sleep(1) # Rate limiting
236
+
```
237
+
238
+
## Docker Compose Example
239
+
240
+
Complete local deployment with Redis and MinIO:
241
+
242
+
```yaml
243
+
# docker-compose.yml
244
+
version: '3.8'
245
+
246
+
services:
247
+
redis:
248
+
image: redis:7-alpine
249
+
command: redis-server --appendonly yes --requirepass ${REDIS_PASSWORD}
250
+
ports:
251
+
- "6379:6379"
252
+
volumes:
253
+
- redis-data:/data
254
+
255
+
minio:
256
+
image: minio/minio
257
+
command: server /data --console-address ":9001"
258
+
ports:
259
+
- "9000:9000"
260
+
- "9001:9001"
261
+
environment:
262
+
MINIO_ROOT_USER: ${MINIO_USER}
263
+
MINIO_ROOT_PASSWORD: ${MINIO_PASSWORD}
264
+
volumes:
265
+
- minio-data:/data
266
+
267
+
volumes:
268
+
redis-data:
269
+
minio-data:
270
+
```
271
+
272
+
```bash
273
+
# .env
274
+
REDIS_PASSWORD=your-redis-password
275
+
MINIO_USER=minioadmin
276
+
MINIO_PASSWORD=your-minio-password
277
+
```
278
+
279
+
## Monitoring
280
+
281
+
### Redis Metrics
282
+
283
+
Key metrics to monitor:
284
+
285
+
- `used_memory`: Memory usage
286
+
- `connected_clients`: Active connections
287
+
- `keyspace_hits/misses`: Cache efficiency
288
+
- `aof_last_write_status`: Persistence health
289
+
290
+
```bash
291
+
redis-cli INFO | grep -E "used_memory|connected_clients|keyspace"
292
+
```
293
+
294
+
### S3 Metrics
295
+
296
+
- Request counts and latency
297
+
- Error rates (4xx, 5xx)
298
+
- Storage usage by prefix
299
+
- Data transfer costs
300
+
301
+
## Security Best Practices
302
+
303
+
1. **Network Isolation**: Run Redis and S3 in private networks
304
+
2. **TLS Everywhere**: Encrypt connections to Redis and S3
305
+
3. **Credential Rotation**: Rotate API keys and passwords regularly
306
+
4. **Access Logging**: Enable S3 access logging for audit trails
307
+
5. **Least Privilege**: Use minimal IAM permissions for S3 access
308
+
309
+
### S3 IAM Policy Example
310
+
311
+
```json
312
+
{
313
+
"Version": "2012-10-17",
314
+
"Statement": [
315
+
{
316
+
"Effect": "Allow",
317
+
"Action": [
318
+
"s3:GetObject",
319
+
"s3:PutObject",
320
+
"s3:ListBucket"
321
+
],
322
+
"Resource": [
323
+
"arn:aws:s3:::my-atdata-bucket",
324
+
"arn:aws:s3:::my-atdata-bucket/*"
325
+
]
326
+
}
327
+
]
328
+
}
329
+
```
+227
docs_src/reference/troubleshooting.qmd
+227
docs_src/reference/troubleshooting.qmd
···
1
+
---
2
+
title: "Troubleshooting & FAQ"
3
+
description: "Common issues and frequently asked questions"
4
+
---
5
+
6
+
This page covers common issues, error messages, and frequently asked questions when working with atdata.
7
+
8
+
## Common Errors
9
+
10
+
### TypeError: 'type' object is not subscriptable
11
+
12
+
**Error:**
13
+
```
14
+
TypeError: 'type' object is not subscriptable
15
+
```
16
+
17
+
**Cause:** Using `Dataset` or `SampleBatch` without subscripting the type parameter on Python < 3.9, or using an unsubscripted generic.
18
+
19
+
**Solution:** Always use the subscripted form:
20
+
```python
21
+
# Correct
22
+
ds = Dataset[MySample]("data.tar")
23
+
batch = SampleBatch[MySample](samples)
24
+
25
+
# Incorrect
26
+
ds = Dataset("data.tar") # Missing type parameter
27
+
```
28
+
29
+
### AttributeError: 'NoneType' object has no attribute...
30
+
31
+
**Error:**
32
+
```
33
+
AttributeError: 'NoneType' object has no attribute '__args__'
34
+
```
35
+
36
+
**Cause:** Creating a `Dataset` or `SampleBatch` without using the subscripted syntax `Class[Type](...)`.
37
+
38
+
**Solution:** These classes use Python's `__orig_class__` mechanism to extract type parameters at runtime. You must use:
39
+
```python
40
+
ds = Dataset[MySample](url) # Correct
41
+
```
42
+
43
+
Not:
44
+
```python
45
+
ds = Dataset(url) # Wrong - no type information
46
+
```
47
+
48
+
### RuntimeError: msgpack field not found in sample
49
+
50
+
**Error:**
51
+
```
52
+
RuntimeError: Malformed sample: 'msgpack' field not found
53
+
```
54
+
55
+
**Cause:** The tar file contains samples that weren't written with atdata's serialization format.
56
+
57
+
**Solution:** Ensure samples are written using `sample.as_wds`:
58
+
```python
59
+
with wds.writer.TarWriter("data.tar") as sink:
60
+
for sample in samples:
61
+
sink.write(sample.as_wds) # Correct
62
+
```
63
+
64
+
### ValueError: Field type not supported
65
+
66
+
**Error:**
67
+
```
68
+
TypeError: Unsupported type for schema field: <class 'SomeType'>
69
+
```
70
+
71
+
**Cause:** Using an unsupported Python type in a PackableSample field.
72
+
73
+
**Supported types:**
74
+
75
+
| Python Type | Notes |
76
+
|-------------|-------|
77
+
| `str` | Unicode strings |
78
+
| `int` | Integers |
79
+
| `float` | Floating point |
80
+
| `bool` | Boolean |
81
+
| `bytes` | Binary data |
82
+
| `NDArray` | Numpy arrays (any dtype) |
83
+
| `list[T]` | Lists of primitives |
84
+
| `T \| None` | Optional fields |
85
+
86
+
**Not supported:** Nested dataclasses, dicts, custom classes.
87
+
88
+
### KeyError when iterating dataset
89
+
90
+
**Error:**
91
+
```
92
+
KeyError: 'msgpack'
93
+
```
94
+
95
+
**Cause:** The WebDataset tar file structure doesn't match expected format.
96
+
97
+
**Solution:** Verify your tar file was created correctly:
98
+
```bash
99
+
# Check tar contents
100
+
tar -tvf data.tar | head -20
101
+
```
102
+
103
+
Each sample should have a `.msgpack` extension in the tar file.
104
+
105
+
## FAQ
106
+
107
+
### How do I check the sample type of a dataset?
108
+
109
+
```python
110
+
ds = Dataset[MySample]("data.tar")
111
+
print(ds.sample_type) # <class 'MySample'>
112
+
```
113
+
114
+
### How do I convert a dataset to a different type?
115
+
116
+
Use the `as_type()` method with a registered lens:
117
+
118
+
```python
119
+
@atdata.lens
120
+
def my_lens(src: SourceType) -> TargetType:
121
+
return TargetType(field=src.other_field)
122
+
123
+
ds_view = ds.as_type(TargetType)
124
+
```
125
+
126
+
### How do I handle optional NDArray fields?
127
+
128
+
Use `NDArray | None` annotation:
129
+
130
+
```python
131
+
@atdata.packable
132
+
class MySample:
133
+
required_array: NDArray
134
+
optional_array: NDArray | None = None
135
+
```
136
+
137
+
### Why is my dataset iteration slow?
138
+
139
+
Common causes:
140
+
141
+
1. **Network latency**: Use local caching for remote datasets
142
+
2. **Small batch sizes**: Increase `batch_size` in `ordered()` or `shuffled()`
143
+
3. **Shuffle buffer**: For `shuffled()`, the `initial` parameter controls buffer size
144
+
145
+
```python
146
+
# Larger batches = better throughput
147
+
for batch in ds.shuffled(batch_size=64, initial=1000):
148
+
...
149
+
```
150
+
151
+
### How do I export to parquet?
152
+
153
+
```python
154
+
ds = Dataset[MySample]("data.tar")
155
+
ds.to_parquet("output.parquet")
156
+
157
+
# With sample limit (for large datasets)
158
+
ds.to_parquet("output.parquet", maxcount=10000)
159
+
```
160
+
161
+
::: {.callout-warning}
162
+
`to_parquet()` loads the dataset into memory. For very large datasets, use `maxcount` to limit samples or process in chunks.
163
+
:::
164
+
165
+
### How do I handle multiple shards?
166
+
167
+
Use WebDataset brace notation:
168
+
169
+
```python
170
+
# Single shard
171
+
ds = Dataset[MySample]("data-000000.tar")
172
+
173
+
# Multiple shards (range)
174
+
ds = Dataset[MySample]("data-{000000..000009}.tar")
175
+
176
+
# Multiple shards (list)
177
+
ds = Dataset[MySample]("data-{000000,000005,000009}.tar")
178
+
```
179
+
180
+
### Can I use S3 or other cloud storage?
181
+
182
+
Yes, use `S3Source` for S3-compatible storage:
183
+
184
+
```python
185
+
from atdata import S3Source, Dataset
186
+
187
+
source = S3Source.from_urls(
188
+
["s3://bucket/data-000000.tar", "s3://bucket/data-000001.tar"],
189
+
endpoint_url="https://s3.example.com", # Optional for non-AWS S3
190
+
)
191
+
192
+
ds = Dataset[MySample](source)
193
+
```
194
+
195
+
### How do I publish to ATProto/Atmosphere?
196
+
197
+
```python
198
+
from atdata.atmosphere import AtmosphereClient, AtmosphereIndex
199
+
200
+
client = AtmosphereClient()
201
+
client.login("handle.bsky.social", "app-password") # Use app password!
202
+
203
+
index = AtmosphereIndex(client)
204
+
205
+
# Publish schema
206
+
schema_uri = index.publish_schema(MySample, version="1.0.0")
207
+
208
+
# Publish dataset
209
+
entry = index.insert_dataset(ds, name="my-dataset", schema_ref=schema_uri)
210
+
```
211
+
212
+
### What's the difference between LocalIndex and AtmosphereIndex?
213
+
214
+
| Feature | LocalIndex | AtmosphereIndex |
215
+
|---------|------------|-----------------|
216
+
| Storage | Redis + S3 | ATProto PDS |
217
+
| Discovery | Local only | Federated network |
218
+
| Auth | None required | ATProto account |
219
+
| Use case | Development, private data | Public distribution |
220
+
221
+
Both implement the `AbstractIndex` protocol, so code can work with either.
222
+
223
+
## Getting Help
224
+
225
+
- **GitHub Issues**: [github.com/your-org/atdata/issues](https://github.com/your-org/atdata/issues)
226
+
- **Documentation**: Check the reference pages for detailed API documentation
227
+
- **Examples**: See the `examples/` directory for working code samples
+46
-3
src/atdata/dataset.py
+46
-3
src/atdata/dataset.py
···
262
262
>>> batch = SampleBatch[MyData]([sample1, sample2, sample3])
263
263
>>> batch.embeddings # Returns stacked numpy array of shape (3, ...)
264
264
>>> batch.names # Returns list of names
265
+
266
+
Note:
267
+
This class uses Python's ``__orig_class__`` mechanism to extract the
268
+
type parameter at runtime. Instances must be created using the
269
+
subscripted syntax ``SampleBatch[MyType](samples)`` rather than
270
+
calling the constructor directly with an unsubscripted class.
265
271
"""
266
272
267
273
def __init__( self, samples: Sequence[DT] ):
···
382
388
...
383
389
>>> # Transform to a different view
384
390
>>> ds_view = ds.as_type(MyDataView)
385
-
391
+
392
+
Note:
393
+
This class uses Python's ``__orig_class__`` mechanism to extract the
394
+
type parameter at runtime. Instances must be created using the
395
+
subscripted syntax ``Dataset[MyType](url)`` rather than calling the
396
+
constructor directly with an unsubscripted class.
386
397
"""
387
398
388
399
@property
···
597
608
maxcount: Optional[int] = None,
598
609
**kwargs,
599
610
):
600
-
"""Save dataset contents to a `parquet` file at `path`
611
+
"""Export dataset contents to parquet format.
612
+
613
+
Converts all samples to a pandas DataFrame and saves to parquet file(s).
614
+
Useful for interoperability with data analysis tools.
601
615
602
-
`kwargs` sent to `pandas.to_parquet`
616
+
Args:
617
+
path: Output path for the parquet file. If ``maxcount`` is specified,
618
+
files are named ``{stem}-{segment:06d}.parquet``.
619
+
sample_map: Optional function to convert samples to dictionaries.
620
+
Defaults to ``dataclasses.asdict``.
621
+
maxcount: If specified, split output into multiple files with at most
622
+
this many samples each. Recommended for large datasets.
623
+
**kwargs: Additional arguments passed to ``pandas.DataFrame.to_parquet()``.
624
+
Common options include ``compression``, ``index``, ``engine``.
625
+
626
+
Warning:
627
+
**Memory Usage**: When ``maxcount=None`` (default), this method loads
628
+
the **entire dataset into memory** as a pandas DataFrame before writing.
629
+
For large datasets, this can cause memory exhaustion.
630
+
631
+
For datasets larger than available RAM, always specify ``maxcount``::
632
+
633
+
# Safe for large datasets - processes in chunks
634
+
ds.to_parquet("output.parquet", maxcount=10000)
635
+
636
+
This creates multiple parquet files: ``output-000000.parquet``,
637
+
``output-000001.parquet``, etc.
638
+
639
+
Example:
640
+
>>> ds = Dataset[MySample]("data.tar")
641
+
>>> # Small dataset - load all at once
642
+
>>> ds.to_parquet("output.parquet")
643
+
>>>
644
+
>>> # Large dataset - process in chunks
645
+
>>> ds.to_parquet("output.parquet", maxcount=50000)
603
646
"""
604
647
##
605
648
+74
tests/EXPECTED_WARNINGS.md
+74
tests/EXPECTED_WARNINGS.md
···
1
+
# Expected Test Warnings
2
+
3
+
This document explains the expected warnings that are suppressed in the test suite using `@pytest.mark.filterwarnings` decorators.
4
+
5
+
## Design Philosophy
6
+
7
+
Per the project's testing conventions (see `CLAUDE.md`), warning suppression is kept **local to individual tests** rather than using global suppression in `conftest.py`. This approach:
8
+
9
+
1. Documents which specific tests have known warning behaviors
10
+
2. Makes it easier to track when warnings appear in unexpected places
11
+
3. Avoids masking genuine warnings from new code
12
+
13
+
## Warning Categories
14
+
15
+
### 1. s3fs/moto Async Incompatibility
16
+
17
+
**Warnings:**
18
+
```python
19
+
@pytest.mark.filterwarnings("ignore::pytest.PytestUnraisableExceptionWarning")
20
+
@pytest.mark.filterwarnings("ignore:coroutine.*was never awaited:RuntimeWarning")
21
+
```
22
+
23
+
**Cause:** The `s3fs` library (used for S3 filesystem access) has async internals that don't fully clean up when used with `moto` (AWS mocking library) in a synchronous test context. When the test tears down, some async coroutines haven't been awaited, triggering these warnings.
24
+
25
+
**Affected tests:** Any test using the `mock_s3` fixture that interacts with `S3DataStore` or `S3FileSystem`.
26
+
27
+
**Impact:** None on test correctness. These are cleanup warnings that occur after the test has completed successfully.
28
+
29
+
**Resolution status:** This is a known interaction between s3fs and moto. A proper fix would require upstream changes to one or both libraries. The warnings are harmless and are expected behavior when using these libraries together.
30
+
31
+
### 2. Deprecated Repo Class
32
+
33
+
**Warning:**
34
+
```python
35
+
@pytest.mark.filterwarnings("ignore:Repo is deprecated:DeprecationWarning")
36
+
```
37
+
38
+
**Cause:** The `Repo` class in `atdata.local` is deprecated in favor of `LocalIndex`. Tests that verify backward compatibility or test the deprecated class directly will trigger this warning.
39
+
40
+
**Affected tests:** Tests in `TestRepoWorkflow`, `TestRepoDeprecation`, and any test explicitly using the `Repo` class.
41
+
42
+
**Impact:** None on test correctness. The deprecation warning is intentional to guide users toward `LocalIndex`.
43
+
44
+
**Resolution status:** These warnings will be removed when the `Repo` class is removed in a future major version. Until then, tests maintain backward compatibility verification.
45
+
46
+
## Adding New Warning Suppressions
47
+
48
+
When adding new `filterwarnings` markers:
49
+
50
+
1. **Verify the warning is expected** - Understand why the warning occurs and confirm it doesn't indicate a real problem
51
+
2. **Use specific patterns** - Target only the exact warning, not broad categories
52
+
3. **Document here** - Add an entry explaining the warning
53
+
4. **Keep it local** - Apply to individual tests, not globally
54
+
55
+
Example:
56
+
```python
57
+
@pytest.mark.filterwarnings("ignore:specific warning pattern:WarningType")
58
+
def test_something():
59
+
...
60
+
```
61
+
62
+
## Files with Warning Suppressions
63
+
64
+
- `tests/test_local.py` - s3fs/moto async warnings, Repo deprecation
65
+
- `tests/test_integration_local.py` - s3fs/moto async warnings, Repo deprecation
66
+
67
+
## Verifying Warnings Are Still Expected
68
+
69
+
Periodically check if upstream fixes have resolved these issues:
70
+
71
+
```bash
72
+
# Run tests without suppressions to see all warnings
73
+
uv run pytest tests/test_local.py -W default 2>&1 | grep -i warning
74
+
```
+146
-6
tests/conftest.py
+146
-6
tests/conftest.py
···
1
-
"""Pytest configuration for atdata tests."""
1
+
"""Pytest configuration for atdata tests.
2
+
3
+
This module provides shared fixtures and sample types for the test suite.
4
+
"""
2
5
3
6
import pytest
4
7
from redis import Redis
···
11
14
12
15
13
16
# =============================================================================
14
-
# Shared sample types for testing
17
+
# Shared Sample Types
15
18
# =============================================================================
19
+
#
20
+
# These shared sample types reduce duplication across test files. Use them
21
+
# when your test doesn't require specific field names or structures.
22
+
#
23
+
# When to use shared types:
24
+
# - General serialization/deserialization tests
25
+
# - Dataset creation and iteration tests
26
+
# - Batch aggregation tests
27
+
# - Integration tests that don't depend on specific field names
28
+
#
29
+
# When to define local types:
30
+
# - Tests that verify specific field name serialization
31
+
# - Tests that need particular field orderings
32
+
# - Tests for edge cases with unusual field combinations
33
+
# - Tests where the type name is significant (e.g., schema name tests)
34
+
#
35
+
# =============================================================================
36
+
16
37
17
38
@atdata.packable
18
39
class SharedBasicSample:
19
-
"""Basic sample with primitive fields for general testing."""
40
+
"""Basic sample with primitive fields for general testing.
41
+
42
+
Fields: name (str), value (int)
43
+
"""
20
44
name: str
21
45
value: int
22
46
23
47
24
48
@atdata.packable
25
49
class SharedNumpySample:
26
-
"""Sample with NDArray field for array serialization testing."""
50
+
"""Sample with NDArray field for array serialization testing.
51
+
52
+
Fields: data (NDArray), label (str)
53
+
"""
27
54
data: NDArray
28
55
label: str
29
56
30
57
31
58
@atdata.packable
32
59
class SharedOptionalSample:
33
-
"""Sample with optional fields for null handling testing."""
60
+
"""Sample with optional fields for null handling testing.
61
+
62
+
Fields: required (str), optional_int (int|None), optional_array (NDArray|None)
63
+
"""
34
64
required: str
35
65
optional_int: Optional[int] = None
36
66
optional_array: Optional[NDArray] = None
···
38
68
39
69
@atdata.packable
40
70
class SharedAllTypesSample:
41
-
"""Sample with all supported primitive types."""
71
+
"""Sample with all supported primitive types.
72
+
73
+
Fields: str_field, int_field, float_field, bool_field, bytes_field
74
+
"""
42
75
str_field: str
43
76
int_field: int
44
77
float_field: float
45
78
bool_field: bool
46
79
bytes_field: bytes
80
+
81
+
82
+
@atdata.packable
83
+
class SharedListSample:
84
+
"""Sample with list fields for array-of-primitives testing.
85
+
86
+
Fields: tags (list[str]), scores (list[float])
87
+
"""
88
+
tags: list[str]
89
+
scores: list[float]
90
+
91
+
92
+
@atdata.packable
93
+
class SharedMetadataSample:
94
+
"""Sample for testing metadata handling.
95
+
96
+
Fields: id (int), content (str), score (float)
97
+
"""
98
+
id: int
99
+
content: str
100
+
score: float
101
+
102
+
103
+
# =============================================================================
104
+
# Tar Creation Helpers
105
+
# =============================================================================
106
+
#
107
+
# These helpers centralize common WebDataset tar file creation patterns.
108
+
# Import and use these instead of duplicating TarWriter boilerplate.
109
+
#
110
+
# =============================================================================
111
+
112
+
import webdataset as wds
113
+
from pathlib import Path
114
+
from typing import Type, TypeVar
115
+
116
+
ST = TypeVar("ST")
117
+
118
+
119
+
def create_tar_with_samples(tar_path: Path, samples: list) -> None:
120
+
"""Create a WebDataset tar file from a list of PackableSample instances.
121
+
122
+
Args:
123
+
tar_path: Path where the tar file will be created.
124
+
samples: List of PackableSample instances to write.
125
+
126
+
Example:
127
+
samples = [SharedBasicSample(name=f"s{i}", value=i) for i in range(10)]
128
+
create_tar_with_samples(tmp_path / "data.tar", samples)
129
+
"""
130
+
tar_path.parent.mkdir(parents=True, exist_ok=True)
131
+
with wds.writer.TarWriter(str(tar_path)) as writer:
132
+
for sample in samples:
133
+
writer.write(sample.as_wds)
134
+
135
+
136
+
def create_basic_dataset(
137
+
tmp_path: Path,
138
+
num_samples: int = 10,
139
+
name: str = "test",
140
+
) -> "atdata.Dataset[SharedBasicSample]":
141
+
"""Create a dataset with SharedBasicSample instances.
142
+
143
+
Args:
144
+
tmp_path: Temporary directory for the tar file.
145
+
num_samples: Number of samples to create.
146
+
name: Prefix for the tar filename.
147
+
148
+
Returns:
149
+
Dataset configured to read the created tar file.
150
+
"""
151
+
tar_path = tmp_path / f"{name}-000000.tar"
152
+
samples = [
153
+
SharedBasicSample(name=f"sample_{i}", value=i * 10)
154
+
for i in range(num_samples)
155
+
]
156
+
create_tar_with_samples(tar_path, samples)
157
+
return atdata.Dataset[SharedBasicSample](url=str(tar_path))
158
+
159
+
160
+
def create_numpy_dataset(
161
+
tmp_path: Path,
162
+
num_samples: int = 5,
163
+
array_shape: tuple = (10, 10),
164
+
name: str = "array",
165
+
) -> "atdata.Dataset[SharedNumpySample]":
166
+
"""Create a dataset with SharedNumpySample instances containing random arrays.
167
+
168
+
Args:
169
+
tmp_path: Temporary directory for the tar file.
170
+
num_samples: Number of samples to create.
171
+
array_shape: Shape of the random numpy arrays.
172
+
name: Prefix for the tar filename.
173
+
174
+
Returns:
175
+
Dataset configured to read the created tar file.
176
+
"""
177
+
tar_path = tmp_path / f"{name}-000000.tar"
178
+
samples = [
179
+
SharedNumpySample(
180
+
data=np.random.randn(*array_shape).astype(np.float32),
181
+
label=f"array_{i}",
182
+
)
183
+
for i in range(num_samples)
184
+
]
185
+
create_tar_with_samples(tar_path, samples)
186
+
return atdata.Dataset[SharedNumpySample](url=str(tar_path))
47
187
48
188
49
189
# =============================================================================
+3
-13
tests/test_integration_edge_cases.py
+3
-13
tests/test_integration_edge_cases.py
···
13
13
14
14
import numpy as np
15
15
from numpy.typing import NDArray
16
-
import webdataset as wds
17
16
18
17
import atdata
19
18
from atdata.local import LocalIndex, LocalDatasetEntry
19
+
20
+
# Use centralized tar creation helper from conftest
21
+
from conftest import create_tar_with_samples
20
22
21
23
22
24
##
···
70
72
"""Sample with NDArray field."""
71
73
label: str
72
74
data: NDArray
73
-
74
-
75
-
##
76
-
# Helper Functions
77
-
78
-
79
-
def create_tar_with_samples(tar_path: Path, samples: list) -> None:
80
-
"""Create a tar file with the given samples."""
81
-
tar_path.parent.mkdir(parents=True, exist_ok=True)
82
-
with wds.writer.TarWriter(str(tar_path)) as writer:
83
-
for sample in samples:
84
-
writer.write(sample.as_wds)
85
75
86
76
87
77
##
+202
-1
tests/test_integration_error_handling.py
+202
-1
tests/test_integration_error_handling.py
···
5
5
- Malformed data (msgpack, tar)
6
6
- Connection failures (Redis, S3, ATProto)
7
7
- Authentication and rate limiting errors
8
+
- Timeout scenarios
9
+
- Partial failures in multi-shard datasets
8
10
"""
9
11
10
12
import pytest
11
-
from unittest.mock import Mock, MagicMock
13
+
from unittest.mock import Mock, MagicMock, patch
12
14
import tarfile
15
+
import io
13
16
14
17
15
18
import atdata
19
+
import webdataset as wds
16
20
from atdata.local import LocalIndex, LocalDatasetEntry
17
21
from atdata.atmosphere import AtmosphereClient, AtUri
18
22
···
421
425
schema = index.get_schema(schema_ref)
422
426
423
427
assert schema["version"] == "1.0.0-beta+build.123"
428
+
429
+
430
+
##
431
+
# Timeout Tests
432
+
433
+
434
+
class TestTimeoutScenarios:
435
+
"""Tests for timeout and slow connection scenarios."""
436
+
437
+
def test_redis_socket_timeout(self):
438
+
"""Redis operations should fail with socket timeout."""
439
+
from redis import Redis
440
+
441
+
# Very short timeout to force failure
442
+
redis = Redis(
443
+
host="10.255.255.1", # Non-routable IP
444
+
port=6379,
445
+
socket_timeout=0.01,
446
+
socket_connect_timeout=0.01,
447
+
)
448
+
449
+
index = LocalIndex(redis=redis)
450
+
451
+
# Should timeout quickly rather than hang
452
+
with pytest.raises(Exception): # TimeoutError or ConnectionError
453
+
index.publish_schema(ErrorTestSample, version="1.0.0")
454
+
455
+
def test_slow_iteration_continues(self, tmp_path):
456
+
"""Dataset iteration should handle slow reads gracefully."""
457
+
# Create a valid dataset
458
+
tar_path = tmp_path / "slow-000000.tar"
459
+
with wds.writer.TarWriter(str(tar_path)) as writer:
460
+
for i in range(5):
461
+
sample = ErrorTestSample(name=f"sample_{i}", value=i)
462
+
writer.write(sample.as_wds)
463
+
464
+
ds = atdata.Dataset[ErrorTestSample](str(tar_path))
465
+
466
+
# Normal iteration should work
467
+
samples = list(ds.ordered(batch_size=None))
468
+
assert len(samples) == 5
469
+
470
+
471
+
##
472
+
# Partial Failure Tests
473
+
474
+
475
+
class TestPartialFailures:
476
+
"""Tests for partial failures in multi-shard scenarios."""
477
+
478
+
def test_multi_shard_with_missing_middle_shard(self, tmp_path):
479
+
"""Multi-shard dataset with missing shard should fail cleanly."""
480
+
# Create first and third shard, skip second
481
+
for i in [0, 2]:
482
+
tar_path = tmp_path / f"data-{i:06d}.tar"
483
+
with wds.writer.TarWriter(str(tar_path)) as writer:
484
+
sample = ErrorTestSample(name=f"shard_{i}", value=i)
485
+
writer.write(sample.as_wds)
486
+
487
+
# Use brace notation that expects all three shards
488
+
url = str(tmp_path / "data-{000000..000002}.tar")
489
+
ds = atdata.Dataset[ErrorTestSample](url)
490
+
491
+
# Should fail when hitting missing shard
492
+
with pytest.raises(FileNotFoundError):
493
+
list(ds.ordered(batch_size=None))
494
+
495
+
def test_multi_shard_with_corrupted_shard(self, tmp_path):
496
+
"""Multi-shard dataset with one corrupted shard should fail."""
497
+
# Create two good shards
498
+
for i in range(2):
499
+
tar_path = tmp_path / f"data-{i:06d}.tar"
500
+
with wds.writer.TarWriter(str(tar_path)) as writer:
501
+
sample = ErrorTestSample(name=f"shard_{i}", value=i)
502
+
writer.write(sample.as_wds)
503
+
504
+
# Create a corrupted third shard
505
+
corrupted_path = tmp_path / "data-000002.tar"
506
+
with open(corrupted_path, "wb") as f:
507
+
f.write(b"this is not a valid tar file")
508
+
509
+
url = str(tmp_path / "data-{000000..000002}.tar")
510
+
ds = atdata.Dataset[ErrorTestSample](url)
511
+
512
+
# Should fail when hitting corrupted shard
513
+
with pytest.raises(Exception): # tarfile.ReadError or similar
514
+
list(ds.ordered(batch_size=None))
515
+
516
+
def test_empty_shard_in_multi_shard(self, tmp_path):
517
+
"""Empty shard in multi-shard dataset should be handled."""
518
+
# Create one shard with data
519
+
tar_path = tmp_path / "data-000000.tar"
520
+
with wds.writer.TarWriter(str(tar_path)) as writer:
521
+
sample = ErrorTestSample(name="sample", value=42)
522
+
writer.write(sample.as_wds)
523
+
524
+
# Create an empty tar (valid but no samples)
525
+
empty_path = tmp_path / "data-000001.tar"
526
+
with tarfile.open(empty_path, "w"):
527
+
pass # Empty tar
528
+
529
+
url = str(tmp_path / "data-{000000..000001}.tar")
530
+
ds = atdata.Dataset[ErrorTestSample](url)
531
+
532
+
# Should handle empty shard gracefully
533
+
samples = list(ds.ordered(batch_size=None))
534
+
# May get 1 sample (from first shard) or error depending on implementation
535
+
assert len(samples) >= 0 # At minimum, shouldn't crash
536
+
537
+
def test_good_shards_before_bad_are_processed(self, tmp_path):
538
+
"""Samples from good shards before bad one should be accessible."""
539
+
# Create first good shard with multiple samples
540
+
tar_path = tmp_path / "data-000000.tar"
541
+
with wds.writer.TarWriter(str(tar_path)) as writer:
542
+
for i in range(3):
543
+
sample = ErrorTestSample(name=f"good_{i}", value=i)
544
+
writer.write(sample.as_wds)
545
+
546
+
# Create second corrupted shard
547
+
corrupted_path = tmp_path / "data-000001.tar"
548
+
with open(corrupted_path, "wb") as f:
549
+
f.write(b"corrupted data")
550
+
551
+
url = str(tmp_path / "data-{000000..000001}.tar")
552
+
ds = atdata.Dataset[ErrorTestSample](url)
553
+
554
+
# Iterate and collect what we can
555
+
collected = []
556
+
try:
557
+
for sample in ds.ordered(batch_size=None):
558
+
collected.append(sample)
559
+
except Exception:
560
+
pass # Expected to fail on second shard
561
+
562
+
# Should have gotten samples from first shard before failure
563
+
# Note: actual behavior depends on WebDataset's buffering
564
+
# This test documents the behavior rather than enforcing it
565
+
assert isinstance(collected, list)
566
+
567
+
568
+
##
569
+
# S3 Error Simulation Tests
570
+
571
+
572
+
class TestS3ErrorSimulation:
573
+
"""Tests for S3-related error scenarios using mocks."""
574
+
575
+
def test_s3_access_denied_error(self):
576
+
"""S3 access denied should raise clear error."""
577
+
from atdata import S3Source
578
+
579
+
# Mock S3 client that raises access denied
580
+
with patch("boto3.client") as mock_boto:
581
+
from botocore.exceptions import ClientError
582
+
583
+
mock_client = Mock()
584
+
mock_client.list_objects_v2.side_effect = ClientError(
585
+
{"Error": {"Code": "AccessDenied", "Message": "Access Denied"}},
586
+
"ListObjects",
587
+
)
588
+
mock_boto.return_value = mock_client
589
+
590
+
source = S3Source(
591
+
bucket="test-bucket",
592
+
keys=["data.tar"],
593
+
credentials={
594
+
"AWS_ACCESS_KEY_ID": "test",
595
+
"AWS_SECRET_ACCESS_KEY": "test",
596
+
},
597
+
)
598
+
599
+
# Opening shard should propagate the error
600
+
with pytest.raises(ClientError):
601
+
source.open_shard("data.tar")
602
+
603
+
def test_s3_connection_timeout_simulation(self):
604
+
"""S3 connection timeout should raise appropriate error."""
605
+
from atdata import S3Source
606
+
607
+
with patch("boto3.client") as mock_boto:
608
+
from botocore.exceptions import ConnectTimeoutError
609
+
610
+
mock_client = Mock()
611
+
mock_client.get_object.side_effect = ConnectTimeoutError(endpoint_url="s3://test")
612
+
mock_boto.return_value = mock_client
613
+
614
+
source = S3Source(
615
+
bucket="test-bucket",
616
+
keys=["data.tar"],
617
+
credentials={
618
+
"AWS_ACCESS_KEY_ID": "test",
619
+
"AWS_SECRET_ACCESS_KEY": "test",
620
+
},
621
+
)
622
+
623
+
with pytest.raises(ConnectTimeoutError):
624
+
source.open_shard("data.tar")
+6
-1
tests/test_integration_local.py
+6
-1
tests/test_integration_local.py
···
53
53
54
54
@pytest.fixture
55
55
def mock_s3():
56
-
"""Provide mock S3 environment using moto."""
56
+
"""Provide mock S3 environment using moto.
57
+
58
+
Note: Tests using this fixture may generate warnings due to s3fs/moto async
59
+
incompatibility. These are suppressed via @pytest.mark.filterwarnings on
60
+
individual tests. See tests/EXPECTED_WARNINGS.md for details.
61
+
"""
57
62
with mock_aws():
58
63
import boto3
59
64
creds = {
+10
-3
tests/test_local.py
+10
-3
tests/test_local.py
···
32
32
33
33
Note: Tests using this fixture may generate warnings due to s3fs/moto async
34
34
incompatibility. These are suppressed via @pytest.mark.filterwarnings on
35
-
individual tests that use this fixture.
35
+
individual tests. See tests/EXPECTED_WARNINGS.md for details.
36
36
"""
37
37
with mock_aws():
38
38
# Create S3 credentials dict (no endpoint_url for moto)
···
78
78
79
79
@dataclass
80
80
class SimpleTestSample(atdata.PackableSample):
81
-
"""Simple test sample for repository tests."""
81
+
"""Simple test sample for repository tests.
82
+
83
+
Note: This matches SharedBasicSample in conftest.py but is kept local
84
+
because tests verify class name behavior.
85
+
"""
82
86
name: str
83
87
value: int
84
88
85
89
86
90
@dataclass
87
91
class ArrayTestSample(atdata.PackableSample):
88
-
"""Test sample with numpy array for repository tests."""
92
+
"""Test sample with numpy array for repository tests.
93
+
94
+
Note: Similar to SharedNumpySample but kept local for test isolation.
95
+
"""
89
96
label: str
90
97
data: NDArray
91
98